Semiparametric estimation and inference using doubly robust moment conditions

Christoph Rothe and Sergio Firpo

No 330, Textos para discussão from FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil)

Abstract: We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance functions that are estimated in a first stage, but retain a fully nonparametric model instead. We call these estimators semiparametric doubly robust estimators (SDREs), and show that they possess superior theoretical and practical properties compared to generic semiparametric two-step estimators. In particular, our estimators have substantially smaller first-order bias, allow for a wider range of nonparametric first-stage estimates, rate-optimal choices of smoothing parameters and data-driven estimates thereof, and their stochastic behavior can be well-approximated by classical first-order asymptotics. SDREs exist for a wide range of parameters of interest, particularly in semiparametric missing data and causal inference models. We illustrate our method with a simulation exercise.

Date: 2013-12-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://repositorio.fgv.br/bitstreams/29ff620b-c00 ... 05b283d4fa9/download (application/pdf)

Related works:
Working Paper: Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fgv:eesptd:330

Access Statistics for this paper

More papers in Textos para discussão from FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil) Contact information at EDIRC.
Bibliographic data for series maintained by Núcleo de Computação da FGV EPGE ().